1. Field of the Invention
The disclosed technology relates to improvements in dynamic range enhancement and is more particularly concerned with dynamic range enhancement through prediction of pixel integration time.
2. Description of the Related Technology
Dynamic range (DR) is one of the most important features of imaging sensors as it provides the ability of a detector to capture highlights and shadows in the same frame. It is determined in accordance with the quality of the elements used to make the imaging sensor, that is, the quality of the pixels. In video systems, the dark-to-light contrast, in the same frame, is typically limited to between 60 and 70 dB. This range is low compared to the dark-to-light contrast that can be obtained with the human eye.
In such video systems, there is a need to have high signal-to-noise ratio (SNR) values over the whole range of operation of the pixels forming the imaging array without loss in image quality or increase in processing resources. Several methods are used to increase the dynamic range of an imaging sensor, for example, logarithmic pixel response, lateral overflow, multiple captures, etc.
Logarithmic pixel response can provide a simple pixel architecture through which high DR can be obtained. Such a method of increasing DR is described in “Wide-Dynamic-Range CMOS Image Sensors—Comparative Performance Analysis” by A Spivak, A Belenky, A Fish, and O Yadid-Pecht, Electron Devices, IEEE Transactions, pages 2446 to 2461, 2009.
However, large noise at low light and image lag are clear disadvantages which are mainly exhibited in the sub-threshold operation where the diffusion current of the transistor is dominant. Furthermore variations of fabrication process parameters increase the fixed pattern noise.
The lateral overflow method collects the charges generated by a high luminance in an extra capacitor as described by S Sugawa, N Akahane, S Adachi, K Mori, T Ishiuchi and K Mizobuchi in “A 100 dB Dynamic Range CMOS Image Sensor using a Lateral Overflow Integration Capacitor”, Solid-State Circuits Conference, Digest of Technical Papers, ISSCC 2005 IEEE International, Volume 1, pages 352 to 603, 2005. This technique introduces a signal-to-noise ratio (SNR) dip at mid-light which degrades the performance of the sensor once the required DR becomes high, that is, more than 100 dB. This is due to the switching between the high conversion gain of the floating diffusion node and the low conversion gain of the lateral overflow capacitor.
A multiple captures techniques (MCT) method is described in “A 640×512 CMOS Image Sensor with Ultra Wide Dynamic Range Floating-Point Pixel-Level ADC” by D X D Yang, A El Gamal, B Fowler and Hui Tian, Solid-State Circuits Conference, Digest of Technical Papers, ISSCC 1999 IEEE International, pages 308 to 309, 1999. MCT allows wide dynamic range imaging by capturing images using different integration times and choosing the value closest to saturation. However, this technique requires a large total frame acquisition time, Tframe, which can be defined as:
      T    frame    =                    ∑                  i          =          1                n            ⁢                        T          int                ⁡                  (          i          )                      +          n      ⁡              (                              T            AD                    +                      T            ro                          )            where n represents the number of captures needed for a certain DR;
Tint(i) represents the integration time of a capture “i”;
TAD represents the analogue-to-digital (AD) conversion time; and
Tro represents the pixel readout time.
Furthermore, the multiple AD conversions needed per pixel, typically, one for each capture, and the image processing required to reconstruct the final image increase the overall power consumption of the imaging sensor.
In order to reduce Tframe, a dual capture technique could be used which has a DR enhancement or extension, DRext, defined by:
      DR    ext                                      =      20    ⁢                  log        10            ⁡              (                              T            max                                T            min                          )            but with a SNR dip, SNRdip, defined by:
      SNR    dip    =      10    ⁢                  log        10            ⁡              (                              T            max                                T            min                          )            where Tmax represents the long capture time and Tmin represents the short capture time. However, for a dual capture process where the second capture time is chosen according to the required DR enhancement or extension, the consequent SNR dip would decrease the image quality at mid-light levels. This means that the DR enhancement or extension obtained depends on the maximum SNR dip in accordance with the specific application.
If a lower SNR dip is required, more captures with integration times within the range between Tmin and Tmax must be performed. This places a greater demand on resources with a lower frame rate and higher power consumption. For example, if an imaging sensor comprises a plurality of pixels that provide a DR of 60 dB and it is desired to increase the DR to 120 dB, ten extra captures are needed to provide a DR enhancement or extension of 60 dB, where each capture provides an extra 6 dB.